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A Bayesian multiscale CNN framework to predict local stress fields in structures with microscale features

Krokos, Vasilis, Xuan, Viet Bui, Bordas, Stephane P. A. ORCID:, Young, Philippe and Kerfriden, Pierre ORCID: 2022. A Bayesian multiscale CNN framework to predict local stress fields in structures with microscale features. Computational Mechanics 69 , pp. 733-766. 10.1007/s00466-021-02112-3

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Multiscale computational modelling is challenging due to the high computational cost of direct numerical simulation by finite elements. To address this issue, concurrent multiscale methods use the solution of cheaper macroscale surrogates as boundary conditions to microscale sliding windows. The microscale problems remain a numerically challenging operation both in terms of implementation and cost. In this work we propose to replace the local microscale solution by an Encoder-Decoder Convolutional Neural Network that will generate fine-scale stress corrections to coarse predictions around unresolved microscale features, without prior parametrisation of local microscale problems. We deploy a Bayesian approach providing credible intervals to evaluate the uncertainty of the predictions, which is then used to investigate the merits of a selective learning framework. We will demonstrate the capability of the approach to predict equivalent stress fields in porous structures using linearised and finite strain elasticity theories.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Engineering
Additional Information: This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.
Publisher: Springer
Date of First Compliant Deposit: 19 October 2021
Date of Acceptance: 17 October 2021
Last Modified: 04 May 2023 18:09

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